Flame monitoring of a gas turbine combustor using a characteristic spectral pattern from a dynamic pressure sensor in the combustor

ABSTRACT

The state of a flame in a gas turbine engine combustor is acoustically monitored using a dynamic pressure sensor within the combustor. A spectral pattern of a dynamic pressure sensor output signal from the sensor is compared with a characteristic frequency pattern that includes information about an acoustic pattern of the flame and information about acoustic signal canceling due to reflections within the combustor. The spectral pattern may also be compared with a characteristic frequency pattern including information about a flame-out condition in the combustor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of copending InternationalApplication PCT/EP2014/054524 entitled “Method for Monitoring a FlameState,” filed on Mar. 10, 2014, which claims priority to European PatentApplication Serial No. 13163529.4 filed on Apr. 12, 2013.

This application incorporates by reference the following co-pendingUnited States utility patent applications in their entirety as if fullyset forth herein:

“Single Dynamic Pressure Sensor Based Flame Monitoring of a Gas TurbineCombustor”, filed concurrently herewith, Attorney Docket No.2014P10020US;

“Flame Monitoring of a Gas Turbine Combustor Using Multiple DynamicPressure Sensors in Multiple Combustors”, filed concurrently herewith,Attorney Docket No. 2014P20106US;

“Nonintrusive Performance Measurement of a Gas Turbine Engine in RealTime”, filed on Jul. 28, 2014, Ser. No. 14/341,950;

“Nonintrusive Transceiver and Method for Characterizing Temperature andVelocity Fields in a Gas Turbine Combustor”, filed on Jul. 28, 2014,Ser. No. 14/341,924;

“Active Measurement Of Gas Flow Temperature, Including In Gas TurbineCombustors”, filed on Mar. 13, 2014, Ser. No. 14/207,741;

“Active Measurement of Gas Flow Velocity or Simultaneous Measurement ofVelocity and Temperature, Including in Gas Turbine Combustors” filed onMar. 13, 2014, Ser. No. 14/207,803;

“Active Temperature Monitoring In Gas Turbine Combustors”, filed on Dec.18, 2013, Ser. No. 14/132,001;

“Multi-Functional Sensor System For Gas Turbine Combustion MonitoringAnd Control” filed on Dec. 18, 2013, Ser. No. 14/109,992;

“Temperature Measurement In A Gas Turbine Engine Combustor”, filed onMar. 14, 2013, Ser. No. 13/804,132; and

“Gas Turbine Engine Control Using Acoustic Pyrometry”, filed on Dec. 14,2010, Ser. No. 12/967,148, Publication No. US2012/0150413.

This application also incorporates by reference in its entirety as iffully set forth herein U.S. Pat. No. 7,853,433, “Combustion AnomalyDetection Via Wavelet Analysis Of Dynamic Sensor Signals”, issued Dec.14, 2010.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a method and apparatus for monitoring a flamestate in a combustion chamber of a gas turbine using dynamic pressuresensors. More specifically, it relates to the use of a dynamic pressuresensor arranged in a pressure influence zone in a combustor of thecombustion chamber, and comparing a spectral pattern of the sensoroutput signal to a characteristic frequency pattern that includesinformation about an acoustic pattern of the flame and information aboutacoustic signal canceling due to reflections within the combustor.

2. Description of the Prior Art

A gas turbine is a flow machine in which a pressurized gas expands. Itcomprises a turbine or expander, a compressor connected upstreamthereof, and a combustion chamber positioned therebetween. The operatingprinciple is based on the cycle process (Joule process): This compressesair by way of the blading of one or more compressor stages, subsequentlymixes said air in the combustion chamber with a gaseous or liquid fuel,ignites and combusts the same. The air is also conducted into asecondary air system and utilized for cooling in particular componentsthat are subject to extreme thermal stresses.

This results in a hot gas (mixture composed of combustion gas and air)which expands in the following turbine section, with thermal energybeing converted into mechanical energy in the process and in the firstinstance driving the compressor. The remaining portion is employed inthe shaft driving mechanism for driving a generator, a propeller orother rotating loads. In the case of the jet power plant, on the otherhand, the thermal energy accelerates the hot gas stream, which generatesthe thrust.

Typically, a plurality of combustors is provided, arranged annularlyaround the turbine axis and having corresponding injector nozzles forfuel. In such a configuration the combustors can be arranged asindividual combustors, referred to as baskets, which are connected onlyshortly before the entry into the turbine (referred to as a can orcan-annular design), or the combustors can be arranged in a commonring-shaped combustion chamber (referred to as an annular design). Whenthe gas turbine is started up, the fuel-air mixture in the respectivecombustion chamber is ignited by means of igniters. Thereafter thecombustion takes place continuously.

The continuous monitoring of the flame, in particular in each individualcombustor in the case of the can-type or can-annular-type design, isimportant for the operational safety of the gas turbine in order toavoid dangerous situations due to the ingress of unburnt fuel in thecombustion chamber or the turbine outlet. In this case the monitoring ofthe flame state must be performed quickly so that no dangerous air-fuelmixtures are produced over a relatively long period of time. Responsetimes of less than a second are desirable. In particular the ignitingand extinguishing of flames must be reliably detected at any given time,especially also in situations such as a load throw-off, during poweringdown of the gas turbine, or in partial extinguishing of individualflames.

Optical and temperature-based systems are known for monitoring the flamestate. Optical systems measure the light emitted by the flame directlyand typically are comparatively quick. A disadvantageous aspect withsystems of said type, however, is the susceptibility of the opticalcomponents to soiling by particles, dust, soot, oil, as well as waterand condensation. The soiling reduces the flame detection capabilitiesof such systems as well as their reliability and operationalavailability.

As an alternative to such optical systems, systems have therefore beendeveloped which are based on the dynamic measurement of the pressure inthe pressure influence zone. A system of said type is described forexample in U.S. Pat. No. 7,853,433 B2. In this case a piezoelectricpressure sensor is arranged in the pressure influence zone of eachcombustor. The time signal of the pressure sensor is digitized andsubjected to a wavelet analysis. The wavelet analysis enables the flamestate and a flame flashback to be detected based on the comparison ofthe normalized amplitudes of the wavelet coefficients with predeterminedthreshold amplitudes. In this case the signals are normalized using themean value of all of the combustors, as a result of which the thresholdvalue is specified. If said threshold values are exceeded it signifies adeviation from the normal state and consequently a change in the flamestate, either the igniting or extinguishing of the flame or a flameflashback.

However, the method described in U.S. Pat. No. 7,853,433 B2 has thedisadvantage that certain flame states are not detected. An extinctionof all of the combustor flames will not be detected, for example.

To reduce costs it is desired to use already existing sensors for flamemonitoring. Dynamic pressure sensors are available from the monitoringof the combustion dynamics and can be used for flame monitoring. Somecurrent combustion dynamics monitoring systems utilize two dynamicpressure sensors per combustor. Future gas turbines will potentiallyutilize only one dynamic pressure sensor per combustor. To prevent theneed for additional instrumentation and thus to keep a cost advantage, aneed exists in the art to detect and monitor a turbine combustor flameusing a single sensor per combustor.

A further need exists in the art to detect a condition of simultaneousflame-out in all combustors using a single sensor per combustor.

There is an additional need in the art to filter the acoustic datareceived from a sensor in a combustor to focus the analysis on specific,localized sound sources while disregarding background noise.

SUMMARY OF THE INVENTION

Accordingly, it is therefore the object of the invention to provide amore reliable and more accurate detection of the flame state for eachindividual flame in the combustion chamber of a gas turbine.

It is a further object of the invention is to provide methods andsystems for detecting and monitoring flames in individual combustors ofa gas turbine combustor chamber wherein only a single sensor is presentin each combustor.

Another object of the invention is to detect and monitor a combustorflame based on the spectral content of acoustic oscillations emitted bythe flame.

Exemplary embodiments of the invention feature a method for monitoring aflame in a gas turbine engine combustor. A dynamic pressure sensoroutput signal is received from an acoustic sensor positioned in the gasturbine engine combustor. The output signal is indicative of acousticoscillations within the combustor. A first comparison is made of aspectral pattern of the dynamic pressure sensor output signal with acharacteristic frequency pattern that includes information about anacoustic spectral pattern of the flame and information about acousticsignal canceling due to reflections of the dynamic pressure sensoroutput signal within the combustor. Based on the first comparison, adetermination is made whether a flame is present in the combustor.

The characteristic frequency pattern may be constructed by recording afirst training spectral pattern of the dynamic pressure sensor outputsignal while the flame is burning; recording a second training spectralpattern of the dynamic pressure sensor output signal while the flame isnot burning; and performing a feature extraction analysis operation onthe first and second training spectral patterns to identify a spectralcharacteristic that links a spectral pattern to a flame state. In thatcase, making the determination whether a flame is present in thecombustor further comprises evaluating a similarity of the spectralcharacteristic to the spectral pattern of the dynamic pressure sensoroutput signal.

Other exemplary embodiments of the invention feature a system formonitoring a flame in a gas turbine engine combustor. The systemincludes an acoustic sensor positioned for measuring acousticoscillations within the gas turbine engine combustor, and a processorconnected for receiving a dynamic pressure sensor output signal from theacoustic sensor. The system further includes computer readable mediacontaining computer readable instructions that, when executed by theprocessor, cause the processor to perform the following operations:receiving a dynamic pressure sensor output signal from the acousticsensor, the output signal being indicative of acoustic oscillationswithin the combustor; making a first comparison of a spectral pattern ofthe dynamic pressure sensor output signal with a characteristicfrequency pattern that includes information about an acoustic spectralpattern of the flame and information about acoustic signal canceling dueto reflections of the dynamic pressure sensor output signal within thecombustor; and, based on the first comparison, making a determinationwhether a flame is present in the combustor.

The respective objects and features of the exemplary embodiments of theinvention may be applied jointly or severally in any combination orsub-combination by those skilled in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is explained in more detail with reference to an exemplaryembodiment illustrated in a drawing, in which:

FIG. 1 shows time signals of two dynamic pressure sensors in the flameexit region of a combustor.

FIG. 2 shows the coherence function of the two time signals in two timesegments.

FIG. 3 shows the evolution in time of the correlation coefficient of thetwo filtered time signals in the case of a load throw-off.

FIG. 4 shows the evolution in time of the correlation coefficient of thetwo filtered time signals in the case of a controlled gas turbineshutdown.

FIG. 5 shows the evolution in time of the correlation coefficient of aplurality of combustors during the ignition sequence.

FIG. 6 shows the evolution in time of the correlation coefficient of aplurality of combustors during the ignition sequence in comparison withdata of optical sensors.

FIG. 7 is a block diagram of a method for monitoring a flame state in agas turbine.

FIG. 8 shows a gas turbine.

FIG. 9 shows the evolution in time of the autocorrelation coefficient ofthe time signals of the pressure sensors in the case of a load throw-off

FIG. 10 shows the evolution in time of the autocorrelation coefficientof the time signals of the pressure sensors in the case of a controlledgas turbine shutdown.

FIG. 11 shows a cross section of a gas turbine engine combustoraccording to one embodiment of the invention.

FIG. 12 is a block diagram of a method for monitoring a flame state in agas turbine according to one embodiment of the invention.

FIG. 13A shows a cross section of a gas turbine engine combustoraccording to one embodiment of the invention.

FIG. 13B shows the evolution in time of a combustor acoustic impulseresponse.

FIG. 14A shows a cross section of a gas turbine engine combustoraccording to one embodiment of the invention.

FIG. 14B is a diagram showing acoustic sensor autocorrelation over timeduring a fast shutdown of a gas turbine.

FIG. 15 is a flow chart showing a method according to one embodiment ofthe invention.

FIG. 16 shows a cross section of a gas turbine engine combustoraccording to one embodiment of the invention.

FIG. 17 shows a cross section of a gas turbine engine combustoraccording to one embodiment of the invention.

FIG. 18 shows a cross section of a gas turbine engine combustoraccording to one embodiment of the invention.

FIG. 19 is a flow chart showing a method according to one embodiment ofthe invention.

FIG. 20 is a chart showing an extracted frequency pattern representing aflame according to one embodiment of the invention.

FIG. 21 is a flow chart showing a method according to one embodiment ofthe invention.

Like parts are labeled with the same reference signs in all the figures.

DETAILED DESCRIPTION

FIG. 1 shows the signals of two dynamic pressure sensors which are basedon piezoelectricity in order to provide an optimal, precise pressuremeasurement. Alternatively, sensors of a different type may be used aspressure sensors provided they permit the current pressure value to beinferred, for example thermocouple elements whose signal also has adependent relationship with the pressure signal. The dynamic pressuresensors are arranged at two different locations in the pressureinfluence zone of a combustor in a gas turbine. What is understood bypressure influence zone in this context is an area whose pressurefluctuations are dependent to a large extent on the dynamics of theflame of the respective combustor. In the case of a gas turbine of thecan-annular type this can be for example an area within the respectivebasket of the combustor. The pressure sensors are typically arrangedupstream of the flame. The gas turbine is explained in detail below withreference to FIG. 8.

FIGS. 1 to 6 each show graphs. In FIG. 1, the signal of each of the twodynamic pressure sensors is plotted against time. Because the signals ofthe pressure sensors are digitized in the method execution sequencedescribed in detail in FIG. 7, the scale of the abscissa in this case isheld directly in 10,000 sampling points. The sampling frequency is 25600Hz in this case. FIG. 1 shows three succeeding data blocks, each 1second long. Shorter data blocks of 0.6 or 0.3 seconds in length areused in the method, which means that in the latter case an evaluationcan take place every 0.3 seconds.

However, the evaluation is not necessarily performed serially, butrather the time periods may also overlap. Thus, for example, data blockshaving a length of 0.6 seconds can be evaluated every 0.3 seconds. Thisenables a fast response speed of the evaluation while at the same timedelivering good statistics.

FIG. 2 shows an example of a filtering of the signals of one data blockfrom FIG. 1 in each case. Two time signals in one data block, i.e. in acommon time segment, are Fourier-transformed and their coherencefunction calculated. The formula of the coherence function reads:

$\gamma = {\sqrt{\frac{G_{xy}^{2}}{G_{xx}G_{yy}}}.}$

Two coherence functions of different data blocks whose values can liebetween 0 and 1 are plotted in the graph of FIG. 2, specifically oncefor a burning flame (upper curve) and once for an extinguished flame(lower curve). The coherence functions are plotted in the range from 0to 500 Hz in each case.

FIG. 2 clearly illustrates the significantly higher coherence of thesignals in the case of a burning flame. The coherence function liesabove 0.8 in a wide range from approx. 80 to 350 Hz, whereas in the caseof an extinguished flame it never climbs above 0.4.

The coherence function shown in FIG. 2 is used in the sequence describedin FIG. 7 for filtering the signal. Outside of ranges in which thecoherence in the case of a burning flame is higher than 0.8, theamplitudes of the frequencies in the Fourier-transformed data blocks areset to 0. Subsequently the thus processed respective data blocks aretransformed back into the time domain once more by means of an inverseFourier transform and processed further there.

The signals now present in the time domain once again are now processedfurther for each data block and each combustor. The correlationcoefficient is formed continuously from the two signals of eachcombustor, per data block in each case, so that an up-to-datecorrelation coefficient is present every 0.3 seconds. Variations withtime of the correlation coefficients are shown in FIGS. 3 to 6.

FIGS. 3 to 5 show the variation with time of the correlationcoefficients for a total of twelve different combustors. The correlationcoefficient is plotted against the time in seconds in each case. A fixedband pass filtering of the signals was carried out between 40 and 600 Hzin each case.

FIG. 3 shows the variation with time for a so-called trip, i.e. a fastshutdown as a consequence, for example, of a threshold value for anoperating parameter such as the rotational speed being exceeded. Atapprox. 24 seconds, a fast shutdown of the gas turbine is executed withinterruption of the fuel supply to the combustors. The graph shows anincrease in the coherence up to practically the value 1 from second 10,which signifies a high flame intensity. This corresponds to the expectedhighly dynamic state during a trip. From the moment of the fast shutdownin which the flames are extinguished, the coherence decreases rapidlyand oscillates around zero. Most of the deflections in this case arebelow 0.2, with the correlation coefficient never deflecting above 0.4.

FIG. 4 shows the variation with time of the correlation coefficients ofthe combustors for a routine controlled shutdown of the gas turbine.During routine operation the correlation coefficients vary between 0.6and 0.8. In the event of a shutdown of the gas turbine with stopping ofthe fuel supply to the combustors at second 52, the correlationcoefficient drops rapidly to values below 0.2. The flames areextinguished.

FIG. 5 shows the variation with time of the correlation coefficients forthe ignition phase, i.e. the startup of the gas turbine. The firstignition takes place at second 15. The exaggerated increase in thecorrelation coefficients of a total of four combustors compared to theother combustors is clearly recognizable. Only these combustors haveignited in the first step, because their correlation coefficientsincrease significantly above a value of 0.2. The ignition time pointsare likewise clearly to be differentiated, as also is the extinction ofthe flames at second 35. At the time of the second ignition aroundsecond 43 it is apparent that only a single combustor ignites.

FIG. 6 shows an alternative method of monitoring during the ignitionsequence. In this instance the root mean squares of the oscillationshave been determined during the data block and weighted with thecorrelation coefficient. This value is marked on the right-hand ordinateand corresponds in a range from 0 to 1.4 to a flame intensity of 0% to100% plotted on the left-hand ordinate. In this case the threshold valuefor the igniting of the flame has been set to a flame intensity of 40%.One skilled in the art will recognize that other weightedrepresentations of the oscillations may be used, such as a logarithmrepresentation or a sinusoidal weighted representation.

The figure shows the root mean squares, weighted with the correlationcoefficient, of the pressure sensors of different combustors (unbrokenlines) in comparison with values from optical sensors (dashed lines,same line thicknesses correspond to same combustors in each case). Thetime axis is specified here in hours:minutes:seconds. During theignition sequence the curves determined as described from the pressuresensors infringe the threshold value up to 1 second earlier than thevalues determined from the optical sensors. The igniting of the flame istherefore detected more quickly. This difference becomes even clearer atthe time of the flame extinction approximately from 10:22:37. In thiscase the values obtained from the pressure sensors are below thethreshold value by up to 5 seconds before the values obtained from theoptical sensors. The detection of an extinguishing flame is considerablyfaster with the described method and consequently increases theoperational safety of the gas turbine significantly.

It holds in all of the above-cited applications of the method that theselected threshold values can be adaptively adjusted for different modesof operation of the gas turbine. In one embodiment variant, for example,other threshold values can apply for the powering up of the gas turbinewhen the combustor flames are ignited than during ongoing operation.This enables empirical values to be taken into account with regard tothe different flame dynamics.

FIG. 7 summarizes the method 1 in a block diagram: For each combustor 2of the gas turbine two piezoelectric-based dynamic pressure sensors 4exist at two different positions in the pressure influence zone thereof.Since the arrangement and the method are identical for all of thecombustors 2, the method is fully illustrated using only the example ofthe combustor 2 depicted in the upper part of the drawing. The remainingeleven combustors 2 in the exemplary embodiment are consolidated as asingle combustor 2 in the block diagram.

The signals of the two pressure sensors 4 are input into an A/Dconverter 6 and digitized. A sampling frequency of 25600 Hz is used inthis case. Subsequently the digitized data is input into a preprocessingmodule 8. Here, the data is divided into data blocks of the respectivetime segment that are approx. 0.3 to 0.6 seconds long. For each datablock, filtering takes place using a fixed bandpass filter between forexample 80 and 350 or 80 and 600 Hz and/or using the dynamic method byway of the coherence of simultaneous data blocks as described in FIG. 2.Suitable resolutions for the Fourier transforms that are necessary herelie between 1 and 6 Hz.

The filtered data blocks having a length of 0.3 to 0.6 seconds are inputinto a calculation module 10. There, the correlation coefficient isformed from the simultaneous data blocks during ongoing operation (upperformula), while root mean squares weighted with the correlationcoefficient are formed during the startup phase (lower formula). A valuethus results per combustor for each data block and hence time segment.Said value is input into an evaluation module 12.

As soon as the input value climbs above 0.4, “Flame ON”, i.e. flameactive, is output as the output signal. If the value drops below 0.2,the output signal is output as “Flame OFF”, i.e. flame extinguished. Inthis case averaging over at least two consecutive data blocks can alsobe performed in addition in the evaluation module 12 in order tominimize statistical fluctuations. The averaging can also take placecontinuously over a plurality of data blocks and be provided with ane.g. exponential weighting so that current data blocks are weightedhigher. The use of threshold output signal values is merely exemplary.Other measures, such as the steepness of a drop over time or thedifference between results from different combustors, may alternativelybe used.

FIG. 8 shows the gas turbine 101. The lower half of FIG. 8 shows a viewfrom above, the upper half a cross-sectional view. A gas turbine 101 isa flow machine. It has a compressor 102 for combustion air, a combustionchamber 104, as well as a turbine unit 106 for driving the compressor102 and a generator (not shown) or a work machine. Toward that end therotating parts of turbine unit 106 and compressor 102 are arranged onthe rotor 108, to which the generator or work machine is also connectedand which is rotatably mounted around its central axis 109. Thecombustion chamber 104 implemented in a can-annular design in theexemplary embodiment comprises a number of tube-shaped individualcombustors 2 which may include baskets. Each of the combustors 2 isequipped for combusting a liquid or gaseous fuel.

The turbine unit 106 has a number of rotatable moving blades 112. Themoving blades 112 are part of the rotor 108 and are arranged annularlyon turbine disks 113, thus forming a number of moving blade rings orrows. In addition the turbine unit 106 comprises a number of stationaryguide vanes 114 which are likewise mounted annularly to a guide vanecarrier 116 of the turbine unit 106, thus forming guide vane rows. Inthis arrangement the moving blades 112 serve to drive the rotor 108through transfer of momentum from the working medium M flowing throughthe turbine unit 106. The guide vanes 114, in contrast, serve to guidethe flow of the working medium M between in each case two succeedingmoving blade rows or moving blade rings, viewed in the flow direction ofthe working medium M. A succeeding pair consisting of a ring of guidevanes 114 or a guide vane row and of a ring of moving blades 112 or amoving blade row is also referred to in this context as a turbine stage.

Each guide vane 114 has a platform 118 which is arranged as a wallelement for fixing the respective guide vane 114 to a guide vane carrier116 of the turbine unit 106.

Each moving blade 112 is mounted in an analogous manner on a turbinedisk 113 by way of a platform 119, which is also referred to as a bladeroot. In such an arrangement the platforms 118, 119 are components thatare subject to comparatively severe thermal stresses and form the outerboundary of a hot gas duct for the working medium M flowing through theturbine unit 106. The rotor 1, which is enclosed by the hot gas duct, isalso subject to extreme thermal stresses, in particular during transientprocesses such as the startup of the gas turbine 101.

A ring segment 121 is arranged in each case on a guide vane carrier 116of the turbine unit 106 between the platforms 118, arranged spaced at adistance from one another, of the guide vanes 114 of two adjacent guidevane rows. The outer surface of each ring segment 121 is in this caselikewise exposed to the hot working medium M flowing through the turbineunit 106 and in the radial direction is spaced apart by a gap from theouter end of the moving blades 112 disposed opposite thereto. The ringsegments 121 arranged between adjacent guide vane rows in this caseserve in particular as cover elements which protect the inner housing inthe guide vane carrier 116 or other built-in housing parts fromexcessive thermal stress due to the hot working medium M flowing throughthe turbine 106.

As already described, the combustion chamber 104 is embodied in theexemplary embodiment as what is termed a can-annular combustion chamber,in which a plurality of combustors 2 arranged around the rotor 1 in thecircumferential direction are arranged individually, leading into theturbine unit 106 on the outlet sides. Here, two described pressuresensors 4 per combustor 2 are arranged in each case in the respectivepressure influence zone thereof, in this instance upstream of the fuelinlet. The shape of the combustion chamber is not critical for theapplicability of the above-described method. The method is equallysuitable for use in gas turbines 101 having other combustion chambershapes such as e.g. annular-type combustion chambers.

If only one pressure sensor 4 is provided per combustor 2 or if onepressure sensor 4 fails, the signal of an adjacent combustor 2 canalternatively be used for the correlation calculation or anautocorrelation of the signals of the same pressure sensor 4 can becalculated and used. This is shown in FIGS. 9 and 10.

FIGS. 9 and 10 show the expected variation with time of theautocorrelation coefficients for a total of twelve different combustors,based on measured cross-correlation values. The autocorrelationcoefficient is in this case the expected correlation coefficient of thesignals of two time segments of the same combustor that are offset withrespect to one another. The autocorrelation coefficient is in each caseplotted against the time in seconds.

FIG. 9 shows, analogously to FIG. 3, the variation with time for a trip.A fast shutdown of the gas turbine with interruption of the fuel supplyto the combustors takes place at approx. 34 seconds. The graph shows anincrease in the coherence up to almost the value 1 from second 25, whichsignifies a high flame intensity. This corresponds to the expectedhighly dynamic state during a trip. However, the increase is not quiteso clear as in FIG. 3, i.e. in the case of the cross-correlation of twosensors. From the moment of the fast shutdown in which the flames areextinguished, the coherence decreases to a value around approx. 0.35.Deflections of individual signals above 0.5 also occur, however. Heretoo, therefore, higher coherence values with extinguished flames areproduced compared to FIG. 3.

A similar picture with even clearer differences results in FIG. 10. FIG.10 shows, analogously to FIG. 4, the variation with time of thecorrelation coefficients of the combustors for a routine controlledshutdown of the gas turbine. During routine operation the correlationcoefficients vary between 0.5 and 0.7, i.e. approx. 0.1 lower than inthe case of the cross-correlation between two sensors. In the event of ashutdown of the gas turbine with stopping of the fuel supply to thecombustors at second 22, the correlation coefficient falls rapidly tovalues around approx. 0.35, likewise again with deflections above 0.5.

Overall, therefore, the autocorrelation of one sensor still allows astable inference to be made with regard to the current flame state.However, the resulting values are not as clear as in the correlation oftwo independent sensors and the threshold values must be adjustedaccordingly. Thus, if two sensors are provided per combustor, theautocorrelation should be used only if one sensor fails. On the otherhand, the use of the autocorrelation makes it possible for existing gasturbines having only one pressure sensor per combustor to undergo asoftware-side retrofit.

Single Sensor Methods

The use of autocorrelation and other single-sensor methods makespossible the detection and monitoring of combustor flames using only onesensor per combustor, reducing the costs of sensors and associatedwiring and interfacing. Furthermore, gas turbine control has beentrending toward the use of only one sensor per combustor. Single sensorflame monitoring makes possible the implementation of flame monitoringin such new turbines without installing additional sensors.

The above-described flame detection technique using dynamic pressuresensors utilizes data from two sensors per combustor to detect aflame-off condition in all combustors simultaneously. A summary of thatconcept is reviewed here with reference to a sectional view of thecombustor 1100 of FIG. 11. Fuel introduced through the ports 1105, 1106is mixed with compressed air in the combustor 1110 and ignited, creatinga flame 1120. Two dynamic pressure sensors 1130, 1131 receive acousticoscillations 1140 generated by the flame 1120 and convert thoseoscillations into signals that can be analyzed by a processor. Asdescribed above, the status of the flame 1120 can be reliably detectedand monitored by combining information about the locations of thesensors 1130, 1131 and the flame 1120 with the spectral contentcontained in the sensor signals. The assumption is made that without theflame 1120 there exists no acoustic source at the flame location thatemits spectral content similar to the flame. That concept has beenvalidated on sensor data from a real gas turbine installation.

A block diagram 1200, shown in FIG. 12, summarizes the disclosed methodsfor determining flame status using a single sensor 1220 per combustor1210. An analog signal received from the sensor is converted to adigital signal by an analog-to-digital (A/D) converter 1230. Theconverter may, for example, be contained within an industrialprogrammable logic controller (PLC) used to control the gas turbine. Thedigital signal is then transmitted to a processor 1240, which may alsobe a processor within a PLC. Additional signals 1235 from other singlesensors in other combustors (not shown) may also be transmitted to theprocessor 1240. The digital signal may be prepared by a preprocessor1250 such as by filtering, scaling, or smoothing. The preprocesseddigital signal is then received by a flame status determinationalgorithm 1260 in the processor 1240 for making a determination of flamestatus. The determination is generally an “on” or “off” determination.The determination may be used by the industrial PLC to generate alarmsor take other action.

Single Sensor Autocorrelation

In cases where only one dynamic pressure sensor per combustor isavailable, one solution for monitoring flame status is to utilizereflections from the walls of the combustor that act as virtualmicrophones. A sectional view of a turbine combustor 1300, shown in FIG.13A, is used to visualize that concept. A dynamic pressure sensor 1310receives acoustic oscillations from the flame 1320 through the directpath 1340 as well as through an indirect path 1341 including reflectionsfrom the combustor wall 1350. The signal contribution from thereflections is delayed compared to the direct path as the reflectedsound travels longer distances.

A graph 1360 (FIG. 13B) of a time response to an impulse signal,measured by a sensor in a basket of a micro turbine, illustrates thisflame monitoring principle. The graph shows the signal that would beobserved by a dynamic pressure sensor if one would emit a short signalpulse. The graph 1360 demonstrates that the impulse is not only observedonce as a direct signal 1370 but also through multiple reflections asindirect signals 1380. The delays of the reflections are dependent onthe physical dimensions of the combustor as well as the source (flame)and receiver (sensor) locations, which are fixed, and also on the gasproperties such as temperature, gas composition and flow. That is, theimpulse response from the flame location to the dynamic pressure sensoris varying due to changing gas properties during operation.

Note that an equal change of the gas properties over all locationsinside the burner results in an equal change of the speed of sound andthus a uniform stretching or compression of the impulse response. Inreal turbine operation, however, the temperature and flow aredistributions rather than constant values. Some signal paths aretherefore affected more strongly than others by changes in gasproperties, resulting in a warping of the impulse response. Inasmuch asthe true flame signal is not known, it can be difficult to accuratelypredict the impulse response using a single sensor. It is known,however, that the gas parameters are limited to a physically feasiblerange. Furthermore, the geometry of the turbine combustor and, ifpresent, the basket, is fixed. That allows the prediction of a timedelay range in which it is physically possible for the main reflectionsto be observed. In the following representation of the recorded signalx(t) it is assumed that the recorded signal is a linear combination ofthe source signal s(t) and its time delayed reflections [s₁(t−t₁), s₂(t−t₂), . . . , s_(N)(t−t_(N))]:

${x(t)} = {{s(t)} + {\sum\limits_{n = 1}^{N}{{s_{n}\left( {t - t_{n}} \right)}.}}}$

That expression may be simplified by separating the amplitude a from thereflections: s₁(t−t₁)→a₁*s(t−t₁). Then

${x(t)} = {{s(t)} + {\sum\limits_{n = 1}^{N}\left( {a_{n}*{s\left( {t - t_{n}} \right)}} \right)}}$

In the presently disclosed autocorrelation method, the reflections s(t−t_(n)) that maximally correlate with the direct path signal s(t) areextracted. Specifically, the time delays of the reflection paths arelimited to the physically possible range, represented by line 1385 ofFIG. 13B, given the minimum and maximum gas parameters and the physicaldimensions of the burner and combustor. Signals indicating reflectionpaths falling outside that range are ignored. Thereafter, the flamestatus can be monitored by correlating the spectral content of the flamefrom the reflected to the direct path signal.

A cross-sectional view of a combustor 1400, shown in FIG. 14A,illustrates a scenario wherein no flame is present but there existsacoustic noise from outside the combustor 1400. The presently describedtechnique assumes that the external acoustic noise 1440 received by thesensor 1410 is not coming from the same combination of locations wherethe flame signal is reflected from the inner burner walls. Furthermore,the technique assumes that different acoustic noise sources areuncorrelated and do not emit in the frequency range of the flame signal.Therefore, the single-sensor flame detection method can distinguishexternal and internal noise from a flame signal and thus detectflameout.

A graph 1460, shown in FIG. 14B, shows autocorrelation values on a timeaxis for a trip, or fast shutdown, of a gas turbine. Afterautocorrelation stabilizes in all combustors, the fuel supply isinterrupted to all combustors. From the time of the trip,autocorrelation decreases rapidly to a measurably lower value.

A flow chart 1500, shown in FIG. 15, illustrates one method inaccordance with the autocorrelation technique described herein. Adynamic pressure sensor output signal is received at block 1510 from asingle acoustic sensor positioned in the gas turbine engine combustor.The output signal is indicative of acoustic oscillations within the gasturbine engine combustor. As discussed above, that output signal may befiltered to exclude frequencies outside an expected frequency rangeemitted by the flame in the gas turbine engine combustor. The dynamicpressure sensor output signal may be received in time-based data blocks,such as blocks having a time period of 1 second or less.

An autocorrelation operation is performed at block 1520 on the dynamicpressure sensor output signal to identify time-separated portions of thesignal. Each time-separated portion of the signal is assigned anautocorrelation value. In one embodiment, useful for monitoring duringan ignition sequence, the autocorrelation operation comprises computingrepresentations of oscillations in the output signal and weighting therepresentations with the autocorrelation values. The representations maycomprise root mean squares, logarithms or sinusoidal weightedrepresentations.

In order to limit the analysis of acoustic reflections to reflectionsthat are physically possible within the combustor, the time-separatedportions of the signal may be filtered to exclude portions having adelay range longer than a maximum threshold delay range. The maximumthreshold delay range is based on physical dimensions of the gas turbineengine combustor and a maximum expected speed of sound in the gasturbine engine combustor.

Based on the autocorrelation values, a determination is made at block1530 that the time-separated portions of the signal include portionsindicative of acoustic oscillations emitted by the flame in the gasturbine engine combustor and received directly by the single acousticsensor, and portions indicative of reflections of the acousticoscillations emitted by the flame. The determination may be made bydetermining that the autocorrelation values of the time-separatedportions of the signal fall above an autocorrelation value threshold.The autocorrelation value threshold may, for example, be a value greaterthan 0.5.

Based on the determination that the time-separated portions of thesignal include portions indicative of acoustic oscillations emitted bythe flame in the gas turbine engine combustor and received directly bythe single acoustic sensor, and portions indicative of reflections ofthe acoustic oscillations emitted by the flame, the technique determinesat block 1540 whether a flame is present in the gas turbine enginecombustor.

Dual Combustor Cross-Correlation

In an alternative signal processing technique for flame monitoring usinga single dynamic pressure sensor per combustor, the signal coherencebetween combustors is utilized. In the combustion chamber 1600 shown inFIG. 16, a flame 1620 is burning in combustor 1610 and a flame 1621 isburning in combustor 1611. A dynamic pressure sensor 1630 recordsacoustic emissions present in combustor 1610 and a dynamic pressuresensor 1631 records acoustic emissions present in combustor 1611. Inaddition to receiving direct and reflected acoustic oscillations such asoscillations 1640, the sensor 1630 also receives oscillations 1641created by the flame 1621 in combustor 1611 and propagated to thecombustor 1610 containing the receiving sensor. Similar cross-propagatedoscillations are received by the other sensors. The cross-correlationbetween the dynamic pressure signal 1640 received by the sensor in thecombustor 1610 of the monitored flame 1620 and a time delayed dynamicpressure signal 1641 of the monitored flame received by a sensor 1631 inanother combustor 1611 is evaluated to determine flame status. The flame1620 is considered online if the cross-correlation of the two signals isabove a threshold. The physically possible time delay for the acousticsignal 1641 to arrive at the dynamic pressure sensor 1631 of the othercombustor 1611 is calculated and used to constrain the problem byfiltering irrelevant signals.

The same flame monitoring arrangement for the combustion chamber 1600may be used to detect a condition in which only one of the twoillustrated combustors contains a flame, as shown in FIG. 17. Note thatwhile the lower combustor 1611 contains a flame 1621, there is no flamein the upper combustor 1610, and therefore no dynamic pressure signalfrom the combustor 1610 that is received in either of the dynamicpressure sensors 1630, 1631.

The time delay constraint ensures that the correlation of the dynamicpressure signal 1741 from the flame 1621 in the lower combustor 1611does not result in a falsely detected flame signal in upper combustor1610. That is, the time delay of a dynamic pressure signal from a flamein the upper combustor 1610 to its nearest dynamic pressure sensor 1630is assumed to be shorter than the delay from that flame location to thedynamic pressure sensor 1631 in the lower combustor 1611. On the otherhand, the acoustic waves from the flame 1621 in the lower combustor 1611would first arrive at the dynamic pressure sensor 1631 in the lowercombustor 1611. Thus the two scenarios can be distinguished and thecombustor without flame can be detected. This technique assumes that theautocorrelation of the monitored flame signal components is narrowenough to distinguish between signals from different combustors.

The presently described arrangement is also effective in determiningflame status in the scenario where all flames in all combustors are off,as illustrated in FIG. 18. In that case, there exists only uncorrelatedacoustic noise 1841 that does not match the physically motivated timedelay range for acoustic waves to travel from a flame to the respectivedynamic pressure sensors 1630, 1631. It is further assumed that thespectral content of the acoustic noise 1841 is distinguishable from aflame signal, providing an additional criterion for identifying theacoustic noise.

The flow chart 1900, shown in FIG. 19, illustrates one method inaccordance with the above-described dual combustor cross-correlationtechnique. The method monitors flames in a plurality of gas turbineengine combustors arranged for combusting fuel in a gas turbine engine.At blocks 1910 and 1920, a processor receives dynamic sensor outputsignals from first and second acoustic sensors positioned in a first andsecond gas turbine engine combustors, respectively. The sensor outputsboth contain information indicative of acoustic oscillations generatedby a first flame within the first gas turbine engine combustor. In thecase of the first sensor, the acoustic oscillations propagate within thecombustor from the flame to the sensor. In the case of the secondsensor, the acoustic oscillations propagate from the flame in the firstcombustor, across space between the first and second combustors, to thesecond sensor in the second combustor.

The dynamic sensor output signals may be received in data blocks of 1second or less in length. To increase efficiency and speed, the firstand second dynamic sensor output signals may be filtered to excludefrequencies outside an expected frequency range emitted by the firstflame within the first gas turbine engine combustor.

The processor then performs a cross-correlation operation on the firstand second dynamic sensor output signals at block 1930, to determine across-correlation value between the first and second acousticoscillations. The cross-correlation operation is constrained by amaximum time delay between correlated components of the first and secondacoustic oscillations. The maximum time delay is based on the physicalparameters of the system. For example, it may be based on the physicalgeometry of the gas turbine engine combustors and the maximum expectedspeed of sound in the gas turbine engine combustors.

The cross-correlation operation on the dynamic pressure sensor outputsignals may further include computing representations of oscillations inthe output signals, and weighting the representations with thecross-correlation values. The representations may comprise root meansquares, logarithms or sinusoidal weighted representations.

A determination is then made at block 1940 whether a flame is present inthe first combustor. Only if the cross-correlation value meets apredetermined criterion, a determination is made that the flame ispresent. Otherwise, the processor determines that a flame-out conditionexists. For example, the criterion may be a threshold cross-correlationvalue such as 0.2. In that case, if the cross-correlation value is below0.2, it is determined that there is no flame in the combustor. Othercriteria may be used to determine flameout, such as a steepness of adrop in cross correlation value over time, or a difference between twocombustors.

As noted, at a given sensor, oscillations from the flame in the samecombustor can be distinguished from oscillations from flames in othercombustors by the order the oscillations are received between twosensors. For example, if first acoustic oscillations generated by afirst flame reach a first acoustic sensor before second acousticoscillations generated by the first flame reach a second acousticsensor, then it can be concluded that the second acoustic oscillationsare generated by the first flame.

The two sensors may be used to monitor flames in both combustors bydetecting oscillations from both flames. Specifically, in addition tothe above, a third dynamic sensor output signal is received from thefirst acoustic sensor positioned in the first gas turbine enginecombustor. The third dynamic sensor output signal contains componentsindicative of third acoustic oscillations generated by a second flamewithin the second gas turbine engine combustor and propagated to thefirst acoustic sensor positioned in the first gas turbine enginecombustor. Further, a fourth dynamic sensor output signal is receivedfrom the second acoustic sensor positioned in the second gas turbineengine combustor. The fourth dynamic sensor output signal containscomponents indicative of fourth acoustic oscillations generated by thesecond flame within the second gas turbine engine combustor. Across-correlation operation is performed on the third and fourth dynamicsensor output signals to determine a cross-correlation value between thethird and fourth acoustic oscillations. The cross-correlation operationis constrained by a maximum time delay between correlated components ofthe third and fourth acoustic oscillations. A determination is made thatthe second flame is present within the second gas turbine enginecombustor only if the cross-correlation value meets a predeterminedcriterion.

The duration of an autocorrelation of the first dynamic sensor outputsignal is preferably sufficiently narrow to permit distinguishing thesignal components indicative of the first acoustic oscillations from thesignal components indicative of the third acoustic oscillations.

Characteristic Spectral Pattern

Another technique for processing the dynamic pressure sensor outputsignals for flame detection does not rely on the time delay of thevarious signal paths from the flame to the dynamic pressure sensor,Instead, it is possible to monitor the spectral characteristics of theflame at the dynamic pressure sensor location. There are two phenomenathat make that possible. First, each flame emits energy with a uniqueand characteristic spectral pattern. Second, as noted above, theacoustic oscillations received by the sensors include both direct andreflected signals. For the different frequencies contained in a signal,the different path lengths of the reflections result in constructive ordestructive signal contributions at the sensor location. For example, ifa signal of frequency F₁ arrives at the sensor through the direct pathwith the same amplitude as a reflected signal in which the reflectedpath introduces a delay of 2/F₁ (i.e., a 180 degree phase shift), thedirect path signal is canceled by the contribution of the reflectedsignal and the sensor cannot see a signal at the frequency F₁.

The acoustic oscillations received at a sensor are therefore a functionof the individual acoustic properties of the flame and also of thebasket or combustor geometry. A “frequency key” or characteristicpattern that includes both the spectral pattern of the flame andinformation on the cancellation may be used to identify and detect theflame in the combustor. An example spectral characteristic pattern 2000,illustrated in FIG. 20, was extracted to represent the flame in aburner. By multiplying that pattern with new data one can determine thecurrent flame status.

One possibility for extracting such a characteristic pattern is to applyfeature extraction techniques to known ground-truth training data. Inone technique, spectral patterns are recorded at the sensor locationwhen the corresponding flame is burning and when the flame is off line.Those samples are processed using a feature extraction algorithm. Onecan also provide training data for difficult-to-detect operationalstates such as for an all-combustor-shutdown where the engine remainsvery noisy but all flames are off line. Additionally, the featureextraction algorithm is provided with information on the flame state(1=On, 0=Off) for each ground-truth spectral pattern. The featureextraction algorithm is then used to find a reduced representation setof spectral features that links the input spectral pattern to the flamestate.

To analyze a live sensor feed, distances are calculated from a spectralrepresentation of the sensor signal to the patterns linked to each flamestate. The closest match is then selected. In one example, a projectiontechnique is used. That is, if one multiplies the extracted spectralcharacteristic with the input spectral pattern from a sensor feed, onereceives the flame state within some small error. Characteristicsassociated with a flame-on condition, when multiplied by the extractedspectral characteristic, yield a value close to 1. Characteristicsassociated with an off-line flame condition, and characteristicsassociated with noise, when multiplied by the extracted spectralcharacteristic, yield a value close to 0. Note that one could usedistance measures other than a projection to evaluate the similarity ofthe extracted spectral characteristics and the currently monitoredfrequency pattern. Examples include a sum of the squared distances, anL1 distance, etc.

Methods for extracting a characteristic function include, for example,Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA)and Generalized Mutual Interdependence Analysis (GMIA), described in H.Claussen, J. Rosca & R. Damper, Signature extraction using mutualinterdependencies, 44 Pattern Recognition 650 (2011), which isincorporated by reference herein. Note that these methods are applied onhigh dimensional data vectors with each frequency component representingone component of the vector.

A flow chart 2100, shown in FIG. 21, shows an example method formonitoring a flame in accordance with the above-described characteristicspectral pattern technique. A dynamic pressure sensor output signal isreceived at block 2010 from an acoustic sensor positioned in thecombustor. The output signal contains components that are indicative ofacoustic oscillations within the combustor. The dynamic pressure sensoroutput signal may be filtered to exclude frequencies outside an expectedfrequency range emitted by the flame in the combustor.

A spectral pattern of the dynamic pressure sensor output signal iscompared, at block 2120, with a characteristic frequency pattern thatincludes information about an acoustic spectral pattern of the flame andinformation about acoustic signal canceling due to reflections of thedynamic pressure sensor output signal within the combustor. The spectralpattern of the dynamic pressure sensor output signal may also becompared with a characteristic frequency pattern that includesinformation about an acoustic spectral pattern present in the combustorduring a flame-out condition in the combustor. The characteristicfrequency patterns may be based on training data with known ground truthregarding the flame conditions. Based on the comparison, a determinationis made at block 2130 whether or not a flame is present in thecombustor.

As discussed above, the characteristic frequency pattern may bedetermined using pattern recognition and feature extraction techniques.In one example, a first training spectral pattern of the dynamicpressure sensor output signal is recorded while the flame is burning,and a second pattern is recorded while the flame is not burning. Thepatterns may be recorded under a plurality of different regimes ofcombustor operation so that the determination whether a flame is presentmay be made under those respective regimes of operation.

A feature extraction analysis operation is performed on the two recordedtraining spectral patterns to identify a spectral characteristic thatcan be used to link a spectral pattern to a flame state. Thedetermination is then made whether a flame is present in the combustorby evaluating a similarity of the spectral characteristic to thespectral pattern of the dynamic pressure sensor output signal. Thesimilarity may be evaluated using a distance measure.

A third training spectral pattern may be recorded while no flame isburning in any combustor of the gas turbine engine combustion chamber.In that case, noise from other components, such as bearings, airturbulence and vibrations, is documented and differentiated from theacoustic characteristics of a flame in the combustor. The featureextraction analysis operation may, for example, be applied to a datavector wherein each component of the vector represents a frequencycomponent of the spectral pattern of the dynamic pressure sensor outputsignal.

Although various embodiments that incorporate the teachings of thepresent invention have been shown and described in detail herein, thoseskilled in the art can readily devise many other varied embodiments thatstill incorporate these teachings. The invention is not limited in itsapplication to the exemplary embodiment details of construction and thearrangement of components set forth in the description or illustrated inthe drawings. The invention is capable of other embodiments and of beingpracticed or of being carried out in various ways. Also, it is to beunderstood that the phraseology and terminology used herein is for thepurpose of description and should not be regarded as limiting. The useof “including,” “comprising,” or “having” and variations thereof hereinis meant to encompass the items listed thereafter and equivalentsthereof as well as additional items. Unless specified or limitedotherwise, the terms “mounted,” “connected,” “supported,” and “coupled”and variations thereof are used broadly and encompass direct andindirect mountings, connections, supports, and couplings. Further,“connected” and “coupled” are not restricted to physical or mechanicalconnections or couplings.

What is claimed is:
 1. A method for monitoring a flame in a gas turbineengine combustor, comprising: receiving a dynamic pressure sensor outputsignal from an acoustic sensor positioned in the gas turbine enginecombustor, the output signal being indicative of acoustic oscillationswithin the combustor; making a first comparison of a spectral pattern ofthe dynamic pressure sensor output signal with a characteristicfrequency pattern that includes information about an acoustic spectralpattern of the flame and information about acoustic signal canceling dueto reflections of the dynamic pressure sensor output signal within thecombustor; and based on the first comparison, making a determinationwhether a flame is present in the combustor.
 2. The method of claim 1,further comprising: making a second comparison of the spectral patternof the dynamic pressure sensor output signal with a characteristicfrequency pattern that includes information about an acoustic spectralpattern present in the combustor during a flame-out condition in thecombustor; and wherein making the determination is based on the firstand second comparisons.
 3. The method of claim 1, further comprising:filtering the dynamic pressure sensor output signal to excludefrequencies outside an expected frequency range emitted by the flame inthe combustor.
 4. The method of claim 1, wherein the characteristicfrequency pattern is based on training data with known ground truthregarding flame condition.
 5. The method of claim 1, further comprisingconstructing the characteristic frequency pattern by: recording a firsttraining spectral pattern of the dynamic pressure sensor output signalwhile the flame is burning; recording a second training spectral patternof the dynamic pressure sensor output signal while the flame is notburning; and performing a feature extraction analysis operation on thefirst and second training spectral patterns to identify a spectralcharacteristic that links a spectral pattern to a flame state; andwherein making the determination whether a flame is present in thecombustor further comprises evaluating a similarity of the spectralcharacteristic to the spectral pattern of the dynamic pressure sensoroutput signal.
 6. The method of claim 5, wherein evaluating a similaritycomprises evaluating a distance measure.
 7. The method of claim 5,wherein recording the first and second spectral training patternscomprises recording under a plurality of different regimes of combustoroperation for use in determining whether a flame is present duringoperation of the combustor under the respective regimes of operation. 8.The method of claim 5, wherein: constructing the characteristicfrequency pattern further comprises recording a third training spectralpattern of the dynamic pressure sensor output signal while no flame isburning in any combustor of a gas turbine engine combustion chamber; andperforming a feature extraction operation on the first and secondtraining spectral patterns further comprises performing a featureextraction operation on the third training spectral pattern.
 9. Themethod of claim 5, wherein the feature extraction analysis operation isselected from a group consisting of linear discriminant analysis andgeneralized mutual interdependence analysis.
 10. The method of claim 5,wherein the feature extraction analysis operation is applied to a datavector wherein each component of the vector represents a frequencycomponent of the spectral pattern of the dynamic pressure sensor outputsignal.
 11. A system for monitoring a flame in a gas turbine enginecombustor, comprising: an acoustic sensor positioned for measuringacoustic oscillations within the gas turbine engine combustor; aprocessor connected for receiving a dynamic pressure sensor outputsignal from the acoustic sensor; computer readable media containingcomputer readable instructions that, when executed by the processor,cause the processor to perform the following operations: receiving adynamic pressure sensor output signal from the acoustic sensor, theoutput signal being indicative of acoustic oscillations within thecombustor; making a first comparison of a spectral pattern of thedynamic pressure sensor output signal with a characteristic frequencypattern that includes information about an acoustic spectral pattern ofthe flame and information about acoustic signal canceling due toreflections of the dynamic pressure sensor output signal within thecombustor; based on the first comparison, making a determination whethera flame is present in the combustor.
 12. The system of claim 11, whereinthe operations further comprise: making a second comparison of thespectral pattern of the dynamic pressure sensor output signal with acharacteristic frequency pattern that includes information about anacoustic spectral pattern present in the gas turbine engine combustorduring a flame-out condition in the combustor; and wherein making thedetermination is based on the first and second comparisons.
 13. Thesystem of claim 11, wherein the operations further comprise: filteringthe dynamic pressure sensor output signal to exclude frequencies outsidean expected frequency range emitted by the flame in the combustor. 14.The system of claim 11, wherein the characteristic frequency pattern isbased on training data with known ground truth regarding flamecondition.
 15. The system of claim 11, wherein the operations furthercomprise constructing the characteristic frequency pattern by: recordinga first training spectral pattern of the dynamic pressure sensor outputsignal while the flame is burning; recording a second training spectralpattern of the dynamic pressure sensor output signal while the flame isnot burning; and performing a feature extraction analysis operation onthe first and second training spectral patterns to identify a spectralcharacteristic that links a spectral pattern to a flame state; andwherein making the determination whether a flame is present in thecombustor further comprises evaluating a similarity of the spectralcharacteristic to the spectral pattern of the dynamic pressure sensoroutput signal.
 16. The system of claim 15, wherein evaluating asimilarity comprises evaluating a distance measure.
 17. The system ofclaim 15, wherein recording the first and second spectral trainingpatterns comprises recording under a plurality of different regimes ofcombustor operation for use in determining whether a flame is presentduring operation of the combustor under the respective regimes ofoperation.
 18. The system of claim 15, wherein: constructing thecharacteristic frequency pattern further comprises recording a thirdtraining spectral pattern of the dynamic pressure sensor output signalwhile no flame is burning in any combustor of a gas turbine enginecombustion chamber; and performing a feature extraction operation on thefirst and second training spectral patterns further comprises performinga feature extraction operation on the third training spectral pattern.19. The system of claim 15, wherein the feature extraction analysisoperation is selected from a group consisting of linear discriminantanalysis and generalized mutual interdependence analysis.
 20. The systemof claim 15, wherein the feature extraction analysis operation isapplied to a data vector wherein each component of the vector representsa frequency component of the spectral pattern of the dynamic pressuresensor output signal.